Fish tracking is a key technology for obtaining movement trajectories and identifying abnormal behavior. However, it faces considerable challenges, including occlusion, multi-scale tracking, and fish deformation. Notably, extant reviews have focused more on behavioral analysis rather than providing a comprehensive overview of computer vision-based fish tracking approaches. This paper presents a comprehensive review of the advancements of fish tracking technologies over the past seven years (2017-2023). It explores diverse fish tracking techniques with an emphasis on fundamental localization and tracking methods. Auxiliary plugins commonly integrated into fish tracking systems, such as underwater image enhancement and re-identification, are also examined. Additionally, this paper summarizes open-source datasets, evaluation metrics, challenges, and applications in fish tracking research. Finally, a comprehensive discussion offers insights and future directions for vision-based fish tracking techniques. We hope that our work could provide a partial reference in the development of fish tracking algorithms.
@article{arxiv.2110.02551,
title = {A Survey of Fish Tracking Techniques Based on Computer Vision},
author = {Weiran Li and Zhenbo Li and Fei Li and Meng Yuan and Chaojun Cen and Yanyu Qi and Qiannan Guo and You Li},
journal= {arXiv preprint arXiv:2110.02551},
year = {2023}
}
Comments
Substantial revisions, deletions and supplements have been made in this version to enhance readability and sharpen the logical flow